Today almost everyone around the globe is using emails with various purposes and hence an efficient growth in the  no. of spam emails is witnessed with time which creates problem for theiusers to find their genuine/ham emails because of which their precious time is wasted and the system becomes less efficient. This is where E-mail spam/ham detectionHcomes into play, playing a significant role in classifying the emails into spam or ham respectively and thus saving users a lot of time to fetch their E-mails. This research paper aims to apply the ML algorithm i.e. multinomial naïve bayes classifier to classify E-mails into spam or ham.We are also going to compareithe 2 methods of vectorizing that are the Bag of Words (BOW)  & Term frequency -Inverse document frequency (TF-IDF) models.

Keywords—email spam detection, ham or spam, bag of words, term frequency -inverse document frequency, multinomial naïve bayes